Graph plays a significant role in representing and analyzing complex relationships in real-world applications such as citation networks, social networks, and biological data. Recently, Large Language Models (LLMs), which have achieved tremendous success in various domains, have also been leveraged in graph-related tasks to surpass traditional Graph Neural Networks (GNNs) based methods and yield state-of-the-art performance. In this survey, we first present a comprehensive review and analysis of existing methods that integrate LLMs with graphs. First of all, we propose a new taxonomy, which organizes existing methods into three categories based on the role (i.e., enhancer, predictor, and alignment component) played by LLMs in graph-related tasks. Then we systematically survey the representative methods along the three categories of the taxonomy. Finally, we discuss the remaining limitations of existing studies and highlight promising avenues for future research. The relevant papers are summarized and will be consistently updated at: https://github.com/yhLeeee/Awesome-LLMs-in-Graph-tasks.

翻译：图在表示和分析现实应用（如引文网络、社交网络和生物数据）中的复杂关系方面发挥着重要作用。近年来，在多个领域取得巨大成功的大型语言模型（LLMs）也被用于图相关任务，超越了基于传统图神经网络（GNNs）的方法，并取得了最先进的性能。在本综述中，我们首先对现有将LLMs与图集成的方法进行了全面回顾与分析。首先，我们提出了一种新的分类体系，将现有方法根据LLMs在图相关任务中扮演的角色（即增强器、预测器和对齐组件）分为三类。随后，我们系统性地沿分类体系的三个类别综述了代表性方法。最后，我们讨论了现有研究的局限性并展望了未来研究的潜在方向。相关论文已汇总并持续更新于：https://github.com/yhLeeee/Awesome-LLMs-in-Graph-tasks。